The results of data analysis can give business the vital insights they need to turn in to successful and profitable ventures. It could be the difference between a successful business operation and a business operation that is in trouble.
Data analysis, though one of the most in-demand job roles in Singapore, doesn’t require a degree in statistics or mathematics to do well, and employers from a wide variety of industries are very keen to recruit data analysts.
Businesses hire data analysts in the field of finance, marketing, administration, HR, IT and procurement, to name just a few. Understand the big picture and provide answers. By engaging in data analysis, you can actually delve deep and discover hidden truths that most business people would never be able to do.
While Data Analyst roles are on the rise, there are certain skills that are vital for anyone who wants to become a data analyst. Before the job, a candidate needs to have either a degree in statistics, business or computer science or a related subject, or work experience in these areas.
If you're interested in becoming a data analyst, you'll need to know:
How much is a data analyst worth? Data analysts earn an average salary of £32,403 per annum, according to jobs site Glassdoor. This pays for a salary, with benefits such as medical insurance and paid leave included in the starting salary. If you think you have the right skills, there are plenty of roles on offer.
Data analysis is an analytical process which involves recording and tabulating (recording and entering, entering and tabulating) the quantities of a product, such as numbers of units produced, costs of materials and expenses.
While data analyst can take different forms, for example in databases, in other structures such as spreadsheets, numbers are the main means of data entry. This involves entering and entering the required data in a data analysis system such as Excel.
For example, although a database doesn’t require a data analyst, it can still benefit from data analysis techniques such as binomial testing, ANOVA and Fisher’s exact tests. Where is the data analysis courses in IT? Given the ever-increasing reliance on technology in business, data analysis courses are vital skills.
The act of grouping a specific set of data in a manner that those elements are more similar to one another than to those in other groups – hence the term ‘cluster.’ Since there is no special target variable while doing clustering, the method is often used to find hidden patterns in the data. The approach is purposely used to offer additional context to a particular trend or dataset.
This type of data analysis method uses historical data to examine and compare a determined segment of users' behavior, which can then be grouped with others with similar characteristics. By using this data analysis methodology, it's possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.
A dependent variable is an element of a complex system that is assumed to have a single cause, but it's affected by multiple factors, thus giving researchers an indication as to how a complex system function.
The regression analysis is used to predict how the value of a dependent variable changes when one or more independent variables change, stay the same or the dependent variable is not moved. Regression is a sophisticated statistical method that includes mathematical functions that are typically called "segmentation," "distribution," and "intercept" functions.
Regression is a type of regression analysis that only contains linear and quadratic functions. You can change the types of factors (or the independent variables) that are selected in regression analysis (it's typically called "nonlinear regression analysis") by changing the order in which the models are constructed.To begin, let's explain how regression analysis works.
The Oracle Corporation is one of the first multinational companies to adopt this type of analysis method, based on which the company was able to develop predictive modelling systems for marketing purposes.
In a more specific sense, a Regression analysis is a popular type of data analysis used for analysing the likelihood that a random variable will move up or down a range of parameters in response to a change in a specific control variable.
Companies who use this type of analysis are looking for trends and patterned performance over time. For example, how a company may respond to a rising cost of labour and its effect on its business bottom line, a weather-related issue like an earthquake, a new advertising campaign, or even a surge in customer demand in some areas.
Too often, analysts make the mistake of presenting information in order to show an abstracted version of it. For instance, say a B2B company has 4 ways to improve their sales funnel:
Then, “informative” would mean that a B2B company needs to optimize their sales funnel to each of these to be more “convenient, faster, easier, more visually engaging, or most cost effective.” Sure, it would be nice if they all improved – they would all provide a competitive advantage in some way. But that’s not what the data tells us.
When you’re presenting data, show as many as possible, in as many charts as possible. Then, try to talk through the implications of the data, rather than overwhelming people with an overwhelming amount of data.
Why? Research suggests that when a number is presented in a visual, people become more likely to process it and learn from it. I recommend using video, text, graphs, and pictures to represent your numbers. This creates a more visually appealing data set. The number of followers on Twitter is visually appealing. The number of followers on Facebook is visually appealing. But nobody looks at their Twitter followers. If you don’t know what your numbers mean, how will your audience? That doesn’t mean numbers aren’t important.
The more data you show to a critical stakeholder, the more likely they are to get lost and distracted from what you’re actually trying to communicate. This is especially important in the case of people in the sales and marketing function.
Do you have a sales person out in the field who is trying to close a deal? It would be a shame if that person got lost in your analytics and lost out on the sale. This problem also occurs on the web.
Consider how web visitors respond to large, colorful charts and graphs. If we’re talking about visualizations that depict web performance, a visual might be helpful. But how often do we see this done? Research shows that people respond better to web-based data in a simplified, less complex format.
There are great stories in the universe. This is an oversimplification, but if you look at history, humans only understand stories. We are great storytellers. We develop, through trial and error, our own intuition about the “right” way to tell stories.
One of the most powerful and effective ways to present data is to go beyond the visual to the audible, that is, to tell stories in a way that people can relate to. Everything you hear about computers being a series of numbers is wrong. We visualize numbers in a precise, quantitative way. But the numbers are not a collection of isolated events. To understand them, we need to understand the broader context.
Businesses and analysts have done this since pie charts first appeared on Microsoft Excel sheets. When presenting data, break down your pie chart into its component segments.
As opposed to an equal-sized circle for the average earnings for all the employees, share a pie chart where the percentages for each individual segment are different, with a link to the corresponding chart.
Pair with explanatory text, show their correlation, and make your choice based on your audience, not on whether you want to scare or “educate” them. The majority of audiences will see the same image, regardless of whether it’s presented in a bar chart, bar chart, line chart, or something else.
Does the data make logical sense? Check your assumptions against the data. Are the graphs charting only part of the story? Include other variables in the graphs. Avoid using axis labels to mislead. Never rely on axes to infer, “logical” conclusions. Trust your eyes: you know what information your brain can process.
Think of numbers like music — they are pleasing, but not overwhelming. Save 3D for the movies. When everyone is enjoying 4K, 8K, and beyond, it’s hard to envision your audience without the new stuff. I remember the first time I got to see HDTV. At home, I sat behind a chair and kept turning around to watch the TV. But at the theatre, I didn’t need a chair. All I had to do was look up, and see the giant screen, the contrast, and the detail.
Charts with colored areas help people focus. Arrows give us scale. Assume your audience doesn’t understand what you’re saying, even if they do. Nobody wants to open a recipe book to learn how to cook soup. Instead, we start with a recipe.
Use a formula to communicate your analysis with as few words as possible. Keep it simple. Resist the urge to over-complicate your presentation. A word cloud is not a word cloud. A bar chart is not a bar chart. If you use a word cloud to illustrate a chart, consider replacing a few words with a gif. A bar chart doesn’t need clouds. And a bar chart doesn’t need clouds. If there’s one thing that’s sure to confuse your audience, it’s bar charts.
Use color with intention. It’s not about pretty. When it comes to presenting data clearly, “informative” is more important than “beautiful.”
However, visualizations like maps, axes, or snapshots can help visual communication to avoid this pitfall. If you are going to show a few locations on a map, make sure each location has a voice and uses a distinct color. Avoid repeating colors from the map or bottom bar in all the visuals. Be consistent with how you present the data. A pie chart is not very interesting if all it shows is a bunch of varying sizes of the pie.
Business leaders are taking note of the importance of data analysis skills in their organisation, as it can make an enormous impact on business.
Larger organisations such as Google, Amazon and Facebook employ huge teams of analysts to create their data and statistics. We are already seeing the rise of the next generation of big data analysts – those who can write code that analyses and visualises the data and report back information to a company to help it improve efficiency and increase revenue.
The increasing need for high-level understanding of data analysis has already led to the role of data analyst becoming available at university level. It is no longer a mandatory business qualification but one that can enhance your CV.
By understanding the importance of each variable, you can improve your business by managing your time and creating more effective systems and processes for running your business. The focus shifts from just providing services to providing value to your customers, creating a better, more intuitive experience for them so they can work with your company for the long-term.
Adopting these small steps will allow you to be more effective in your business and go from being an employee to an entrepreneur.